What are some resources on computational learning theory?
Although I have only partially read (or not read at all) some of the following resources and some of these resources may not cover more advanced topics than the ones presented in the book you are reading, I think they can still be useful for your purposes, so I will share them with you.
I would also like to note that if you understand the contents of the book you are currently reading, you are probably already prepared for reading some (if not most of) the research papers you wish to read. Initially, you may find them a little bit too succinct and sometimes unclear or complex, but you need to get used to this format, so there's nothing stopping you from trying to read them and learn even more by doing this exercise.
Books

An Introduction to Computational Learning Theory (1994) by Kearns and Vazirani

No free PDF available


The Nature of Statistical Learning Theory (1995, 2000) by Vapnik
Machine Learning (1997) by Mitchell
Statistical Learning Theory (1998) by Vapnik
Prediction, Learning, and Games (2006) by Cesa-Bianchi and Lugosi
Foundations of Machine Learning (2012) by Mohri et al.
Boosting: Foundations and Algorithms (2012) by Schapire and Freund
Bandit Algorithms (2020) by Lattimore and Szepesvári (with associated solutions to the exercises)

Papers

A theory of the learnable (1984) by Valiant
An overview of statistical learning theory (1999) by Vapnik
Introduction to Statistical Learning Theory (2014) by Bousquet et al.

Courses (videos)

Learning from Data (Caltech) (2012) by Yaser Abu-Mostafa
Machine Learning Theory (2015) by Shai Ben-David

Lecture notes

Introduction to Machine Learning (2009) by Shai Shalev-Shwartz

Concentration-of-measure inequalities (2009) by Gábor Lugosi

Stat 928: Statistical Learning Theory (2011) by Sham Kakades

COMS E6253, Advanced Topics in Computational Learning Theory (2012) Rocco Servedio

9.520: Statistical Learning Theory and Applications, Spring 2012 by Tomaso Poggio et al.

STAT928: Statistical Learning Theory and Sequential Prediction (2014) by Alexander Rakhlin and Karthik Sridharan

ECE 543, Statistical Learning Theory (2019) by Bruce Hajek and Maxim Raginsky


Other
See also this list of resources https://kiranvodrahalli.github.io/links/#resources-notes-textbooks-monographs-classes-etc compiled by Kiran Vodrahalli.